Talking Heads – Systematic investing - Bigger, stronger, faster

This is an edited audio transcript of the Talking Heads podcast episode: Taking systematic investing out of the black box

Andrew Craig: Hello, welcome to the BNP Paribas Asset Management Talking Heads podcast. Every week, Talking Heads will bring you in-depth insights and analysis through the lens of sustainability on the topics that really matter to investors. In this episode, we’ll be discussing quantitative portfolio management, also known as systematic investing. I’m Andy Craig, Co-head of the Investment Insight Centre, and I’m joined today by Carmine De Franco, who recently joined BNP Paribas Asset Management as Head of Quantitative Equity Portfolio Management. Welcome, Carmine, and thanks for joining me.

Carmine De Franco: Hi, Andrew.

AC: Carmine, let’s start at the beginning. To put it very simply, there are two very distinct approaches to making investment decisions. The traditional approach is judgmental active management, where individuals take decisions based on their knowledge and experience of financial markets. The advent of high-performance computers and access to sufficient data has led to quantitative or systematic portfolio management, where decisions are based on the analysis of data, identification of patterns and trends, and scientific testing. That’s just a basic overview. Can you talk us through what systematic investment is?

CDF: The best way to is to use examples. When you think about a traditional portfolio peripheral manager, what he does is to find investment ideas about stocks, about sectors, about countries, and then implement them and through skill and knowledge, [and] experience, try to generate alpha. Systematic portfolio management is different because we try to identify regular and consistent performance drivers in the market and then we implement them with discipline. What are those engines of performance? Well, you might be aware of some of them. If you think about value, the stock market typically is good on average at putting a fair price on securities, but over time and across companies, market prices never reflect the underlying value. This is where opportunity arises. Think about trends in the market that naturally develop. These are the kind of things that we tried to measure, to capture and then we implement them in a portfolio in a systematic way.

AC: Isn’t it the risk that for those who are not comfortable with the science of quantitative portfolio management that it actually appears like a  black box? It’s very technical. We naturally grasp the idea of humans who have done the right studies and have the right experience being able to anticipate the market’s movements. What about this black box sort of fear of not really understanding what’s driving the decision making?

CDF: Of course, there’s always a risk that, if you run a very complex model, very sophisticated algorithms, you end up doing what we call data mining, right? But that’s where the difference arises between different asset managers and different strategies and approaches. We believe that the most important thing is the choice of these engines, of these factors. For us, it’s important that they have a clear economic rationale.  These ideas have been established many decades ago. They have a sound academic framework. Just to give an example, we were talking about value investing or trends back in the 30s, so almost a century ago. And there have been a few Nobel prizes that have been awarded based on this research. So, this is something that has been with us with the market for many, many decades.
Another thing that’s important to mention that even though it might be complex to implement the ideas, they are highly complementary. It gives you a [investment] profile that’s much more diversified.

AC: Let’s move to the merits and the attractions for investors. Why would investors look at systematic investing as an attractive approach for the management of their assets?

CDF: If we look at what clients, investors worldwide, are doing, from a portfolio construction perspective, they are moving more into a core/satellite [approach]. The core is being implemented with [an] passive or indexed approach. On the satellite, they are looking for  diverse strategies that could bring alpha. By its nature, factor investing offers a source of alpha that is usually not found in the traditional discretionary approach. From that perspective, it makes sense to consider this as a way to diversify your sources of alpha.

The second point [is] living [in] times of uncertainty –US elections, monetary policy, geopolitical events, economic growth in China, you name it. So, typically in these conditions, markets might react and overreact to the latest news, which offers interesting opportunities if you can implement your strategy in a disciplined way.

The third point is looking at what happened over the last 12 to 24 months around the tech theme and artificial intelligence. Of course, this is something that will fundamentally structure and change our lives and the  world of finance. But what’s the next move? What should investor do? Should they double-down? Should they pile on? Or should they take profits and look from opportunity elsewhere? This is a recurring theme when we discuss with our investors. And, from that perspective, doing something that is systematic in nature would bring some needed diversification.

AC: Yes, not putting all one’s eggs in one basket makes a lot of sense. You mentioned the theme. Quantitative investing has obviously received a huge boost through the developments in computing and access to greater sets of data. What about artificial intelligence? How do you see artificial intelligence influencing and shaping quantitative investing?

CDF: AI can have a significant impact. But it’s a tool. You have to be careful how to use it. It will make it possible to better measure and capture these drivers of performance, for example, by exploiting data that is difficult to manipulate. Think about credit card data, satellite images and the like. Also, one thing that is interesting is that you can capture obvious cause-effect relationships. We people in finance like to say things like, if this indicator goes up by 1%, that variable goes up by 3%. This is what we call the linear relationship. But typically, linear relationship are very rare in finance. They’re more complex. So, for these things, artificial intelligence can really help uncover patterns. There are already some approaches, especially related to machine learning. Are we saying that algorithms will take away all the jobs of portfolio managers?

When it comes to systematic investment, there’s an important role for research and innovation which fuel the continuous improvements on our approach. How can we make better use of new data? You can only do [it] if you are at the forefront of research and if you can rely on a team of experienced professionals.

AC: Let’s talk about environmental, societal and governance considerations, which are important for us in portfolio management. How does that fit in with a systematic investing framework?

CDF: There is a belief in the market that when you are systematic, you can’t be sustainable. In reality, those two things go hand in hand because from a practical perspective, sustainable investment integration is often achieved through measurable objectives, whether you are increasing the ESG score, whether you are reducing the carbon footprint. From a systematic [investing] perspective, you can integrate them in your framework alongside the traditional risk-return objectives. ESG is in an integral part of our day-to-day job.

AC: Thank you very much for joining me.

CDF: Thank you, Andrew.